Beamforming Applied to Ultrasound Analysis in Detection of Bearing Defects

The bearings of rotating machinery often fail, leading to unforeseen downtime of large machines in industrial plants. Therefore, condition monitoring can be a powerful tool to aid in the quick identification of these faults and make it possible to plan maintenance before the fault becomes too drasti...
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Autor*in:	Thomas Verellen [verfasserIn] Florian Verbelen [verfasserIn] Kurt Stockman [verfasserIn] Jan Steckel [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	acoustic signal processing array signal processing beamforming microphone arrays predictive maintenance

Übergeordnetes Werk:	In: Sensors - MDPI AG, 2003, 21(2021), 20, p 6803
Übergeordnetes Werk:	volume:21 ; year:2021 ; number:20, p 6803

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/s21206803

Katalog-ID:	DOAJ030630053

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language	English
source	In Sensors 21(2021), 20, p 6803 volume:21 year:2021 number:20, p 6803
sourceStr	In Sensors 21(2021), 20, p 6803 volume:21 year:2021 number:20, p 6803
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	acoustic signal processing array signal processing beamforming microphone arrays predictive maintenance Chemical technology
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authorswithroles_txt_mv	Thomas Verellen @@aut@@ Florian Verbelen @@aut@@ Kurt Stockman @@aut@@ Jan Steckel @@aut@@
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callnumber-first	T - Technology
author	Thomas Verellen
spellingShingle	Thomas Verellen misc TP1-1185 misc acoustic signal processing misc array signal processing misc beamforming misc microphone arrays misc predictive maintenance misc Chemical technology Beamforming Applied to Ultrasound Analysis in Detection of Bearing Defects
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topic_title	TP1-1185 Beamforming Applied to Ultrasound Analysis in Detection of Bearing Defects acoustic signal processing array signal processing beamforming microphone arrays predictive maintenance
topic	misc TP1-1185 misc acoustic signal processing misc array signal processing misc beamforming misc microphone arrays misc predictive maintenance misc Chemical technology
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title	Beamforming Applied to Ultrasound Analysis in Detection of Bearing Defects
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title_auth	Beamforming Applied to Ultrasound Analysis in Detection of Bearing Defects
abstract	The bearings of rotating machinery often fail, leading to unforeseen downtime of large machines in industrial plants. Therefore, condition monitoring can be a powerful tool to aid in the quick identification of these faults and make it possible to plan maintenance before the fault becomes too drastic, reducing downtime and cost. Predictive maintenance is often based on information gathered from accelerometers. However, these sensors are contact-based, making them less attractive for use in an industrial plant and more prone to breakage. In this paper, condition monitoring based on ultrasound is researched, where non-invasive sensors are used to record the noise originating from different defects of the Machinery Fault Simulator. The acoustic data are recorded using a sparse microphone array in a lab environment. The same array was used to record real spatial noise in a fully operational plant which was later added to the acoustic data containing the different defects with a variety of Signal To Noise ratios. In this paper, we compare the classification results of the noisy acoustic data of only one microphone to the beamformed acoustic data. We do this to investigate how beamforming could improve the classification process in an ultrasound condition-monitoring application in a real industrial plant.
abstractGer	The bearings of rotating machinery often fail, leading to unforeseen downtime of large machines in industrial plants. Therefore, condition monitoring can be a powerful tool to aid in the quick identification of these faults and make it possible to plan maintenance before the fault becomes too drastic, reducing downtime and cost. Predictive maintenance is often based on information gathered from accelerometers. However, these sensors are contact-based, making them less attractive for use in an industrial plant and more prone to breakage. In this paper, condition monitoring based on ultrasound is researched, where non-invasive sensors are used to record the noise originating from different defects of the Machinery Fault Simulator. The acoustic data are recorded using a sparse microphone array in a lab environment. The same array was used to record real spatial noise in a fully operational plant which was later added to the acoustic data containing the different defects with a variety of Signal To Noise ratios. In this paper, we compare the classification results of the noisy acoustic data of only one microphone to the beamformed acoustic data. We do this to investigate how beamforming could improve the classification process in an ultrasound condition-monitoring application in a real industrial plant.
abstract_unstemmed	The bearings of rotating machinery often fail, leading to unforeseen downtime of large machines in industrial plants. Therefore, condition monitoring can be a powerful tool to aid in the quick identification of these faults and make it possible to plan maintenance before the fault becomes too drastic, reducing downtime and cost. Predictive maintenance is often based on information gathered from accelerometers. However, these sensors are contact-based, making them less attractive for use in an industrial plant and more prone to breakage. In this paper, condition monitoring based on ultrasound is researched, where non-invasive sensors are used to record the noise originating from different defects of the Machinery Fault Simulator. The acoustic data are recorded using a sparse microphone array in a lab environment. The same array was used to record real spatial noise in a fully operational plant which was later added to the acoustic data containing the different defects with a variety of Signal To Noise ratios. In this paper, we compare the classification results of the noisy acoustic data of only one microphone to the beamformed acoustic data. We do this to investigate how beamforming could improve the classification process in an ultrasound condition-monitoring application in a real industrial plant.
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title_short	Beamforming Applied to Ultrasound Analysis in Detection of Bearing Defects
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